{"title":"Measuring Operating Leverage","authors":"Chen H, Chen J, Li F, et al.","doi":"10.1093/rapstu/raab025","DOIUrl":"https://doi.org/10.1093/rapstu/raab025","url":null,"abstract":"<span><div>Abstract</div>We examine a simple measure of operating leverage: the ratio of fixed costs (measured by depreciation and amortization plus selling, general, and administrative expenses) to the market (or book) value of assets. We find that this measure of operating leverage positively predicts returns. This operating leverage measure is not explained by common factors and performs better than the traditional measures of operating leverage. Furthermore, an exploratory two-factor model with the operating leverage factor works at least as well as, but does not subsume, the Fama and French five-factor model. (<span style=\"font-style:italic;\">JEL</span> G11, G12, G30)</span>","PeriodicalId":21144,"journal":{"name":"Review of Asset Pricing Studies","volume":"8 4","pages":""},"PeriodicalIF":13.1,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cross-Sectional Skewness","authors":"Oh S, Wachter J, Chen H.","doi":"10.1093/rapstu/raab023","DOIUrl":"https://doi.org/10.1093/rapstu/raab023","url":null,"abstract":"<span><div>Abstract</div>What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (<span style=\"font-style:italic;\">JEL</span>, G10, G11, G12, G13, G14).</span>","PeriodicalId":21144,"journal":{"name":"Review of Asset Pricing Studies","volume":"4 1","pages":""},"PeriodicalIF":13.1,"publicationDate":"2021-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Firm Characteristics and Global Stock Returns: A Conditional Asset Pricing Model","authors":"Steffen Windmüller","doi":"10.1093/rapstu/raab024","DOIUrl":"https://doi.org/10.1093/rapstu/raab024","url":null,"abstract":"This paper studies the relation between 36 firm-level characteristics and stock returns in 48 countries using instrumented principal components analysis. A non-U.S. country-neutral conditional factor model performs well in describing risk and returns and generates small and statistically insignificant anomaly intercepts when allowing for three or more latent factors. The non-U.S. model performs better in emerging than in developed markets, while showing substantial differences across countries. On average, only 10 characteristics significantly contribute to the models’ performance. Market beta, momentum, and firm size characteristics instrument for systemic exposure in U.S. and non-U.S. models, while investment and book-to-market do not. (JEL G11, G12, G14, G15)","PeriodicalId":21144,"journal":{"name":"Review of Asset Pricing Studies","volume":"11 1","pages":""},"PeriodicalIF":13.1,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Embedded Leverage","authors":"Frazzini A, Pedersen L, Pontiff J.","doi":"10.1093/rapstu/raab022","DOIUrl":"https://doi.org/10.1093/rapstu/raab022","url":null,"abstract":"<span><div>Abstract</div>Many financial instruments are designed with embedded leverage, such as options and leveraged exchange-traded funds (ETFs). Embedded leverage alleviates investors’ leverage constraints, and, therefore, we hypothesize that embedded leverage lowers required returns. Consistent with this hypothesis, we find empirically that options and leveraged ETFs provide significant amounts of embedded leverage; this embedded leverage increases return volatility in proportion to the embedded leverage; and higher embedded leverage is associated with lower risk-adjusted returns. The results are statistically and economically significant, and we provide extensive robustness tests and discuss the broader implications of embedded leverage for financial economics. (<span style=\"font-style:italic;\">JEL</span> G02, G11, G12, G13, G14, G20)</span>","PeriodicalId":21144,"journal":{"name":"Review of Asset Pricing Studies","volume":"5 2","pages":""},"PeriodicalIF":13.1,"publicationDate":"2021-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138512339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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